






'''simple docstring'''





import argparse
import json
import pickle
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import MaskFormerConfig, MaskFormerForInstanceSegmentation, MaskFormerImageProcessor, SwinConfig
from transformers.utils import logging


logging.set_verbosity_info()
_UpperCamelCase						:						Tuple  =					logging.get_logger(__name__)
def __UpperCAmelCase   (					A					:			str		)    ->   int:
			UpperCAmelCase_      :       Optional[int]          =       SwinConfig.from_pretrained(
			    '''microsoft/swin-tiny-patch4-window7-224'''			,       out_features=['''stage1''', '''stage2''', '''stage3''', '''stage4''']		)
			UpperCAmelCase_      :       List[Any]          =       MaskFormerConfig(backbone_config=A		)

			UpperCAmelCase_      :       str          =       '''huggingface/label-files'''
			if "ade20k-full" in model_name:
						# this should be ok
						UpperCAmelCase_      :       List[Any]          =       8_4_7
						UpperCAmelCase_      :       Dict          =       '''maskformer-ade20k-full-id2label.json'''
			elif "ade" in model_name:
						# this should be ok
						UpperCAmelCase_      :       Dict          =       1_5_0
						UpperCAmelCase_      :       int          =       '''ade20k-id2label.json'''
			elif "coco-stuff" in model_name:
						# this should be ok
						UpperCAmelCase_      :       Optional[int]          =       1_7_1
						UpperCAmelCase_      :       Dict          =       '''maskformer-coco-stuff-id2label.json'''
			elif "coco" in model_name:
						# TODO
						UpperCAmelCase_      :       Tuple          =       1_3_3
						UpperCAmelCase_      :       Optional[int]          =       '''coco-panoptic-id2label.json'''
			elif "cityscapes" in model_name:
						# this should be ok
						UpperCAmelCase_      :       Dict          =       1_9
						UpperCAmelCase_      :       List[Any]          =       '''cityscapes-id2label.json'''
			elif "vistas" in model_name:
						# this should be ok
						UpperCAmelCase_      :       Dict          =       6_5
						UpperCAmelCase_      :       Union[str, Any]          =       '''mapillary-vistas-id2label.json'''

			UpperCAmelCase_      :       Optional[Any]          =       json.load(open(hf_hub_download(A			,       A			,       repo_type='''dataset'''		)			,       '''r'''		)		)
			UpperCAmelCase_      :       Any          =       {int(A		): v for k, v in idalabel.items()}

			return config
def __UpperCAmelCase   (					A					:			Any		)    ->   Optional[Any]:
			UpperCAmelCase_      :       List[str]          =       []
			# stem
			# fmt: off
			rename_keys.append(('''backbone.patch_embed.proj.weight''', '''model.pixel_level_module.encoder.model.embeddings.patch_embeddings.projection.weight''')		)
			rename_keys.append(('''backbone.patch_embed.proj.bias''', '''model.pixel_level_module.encoder.model.embeddings.patch_embeddings.projection.bias''')		)
			rename_keys.append(('''backbone.patch_embed.norm.weight''', '''model.pixel_level_module.encoder.model.embeddings.norm.weight''')		)
			rename_keys.append(('''backbone.patch_embed.norm.bias''', '''model.pixel_level_module.encoder.model.embeddings.norm.bias''')		)
			# stages
			for i in range(len(config.backbone_config.depths		)		):
						for j in range(config.backbone_config.depths[i]		):
									rename_keys.append((F"backbone.layers.{i}.blocks.{j}.norm1.weight", F"model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.layernorm_before.weight")		)
									rename_keys.append((F"backbone.layers.{i}.blocks.{j}.norm1.bias", F"model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.layernorm_before.bias")		)
									rename_keys.append((F"backbone.layers.{i}.blocks.{j}.attn.relative_position_bias_table", F"model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_bias_table")		)
									rename_keys.append((F"backbone.layers.{i}.blocks.{j}.attn.relative_position_index", F"model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_index")		)
									rename_keys.append((F"backbone.layers.{i}.blocks.{j}.attn.proj.weight", F"model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.attention.output.dense.weight")		)
									rename_keys.append((F"backbone.layers.{i}.blocks.{j}.attn.proj.bias", F"model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.attention.output.dense.bias")		)
									rename_keys.append((F"backbone.layers.{i}.blocks.{j}.norm2.weight", F"model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.layernorm_after.weight")		)
									rename_keys.append((F"backbone.layers.{i}.blocks.{j}.norm2.bias", F"model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.layernorm_after.bias")		)
									rename_keys.append((F"backbone.layers.{i}.blocks.{j}.mlp.fc1.weight", F"model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.intermediate.dense.weight")		)
									rename_keys.append((F"backbone.layers.{i}.blocks.{j}.mlp.fc1.bias", F"model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.intermediate.dense.bias")		)
									rename_keys.append((F"backbone.layers.{i}.blocks.{j}.mlp.fc2.weight", F"model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.output.dense.weight")		)
									rename_keys.append((F"backbone.layers.{i}.blocks.{j}.mlp.fc2.bias", F"model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.output.dense.bias")		)

						if i < 3:
									rename_keys.append((F"backbone.layers.{i}.downsample.reduction.weight", F"model.pixel_level_module.encoder.model.encoder.layers.{i}.downsample.reduction.weight")		)
									rename_keys.append((F"backbone.layers.{i}.downsample.norm.weight", F"model.pixel_level_module.encoder.model.encoder.layers.{i}.downsample.norm.weight")		)
									rename_keys.append((F"backbone.layers.{i}.downsample.norm.bias", F"model.pixel_level_module.encoder.model.encoder.layers.{i}.downsample.norm.bias")		)
						rename_keys.append((F"backbone.norm{i}.weight", F"model.pixel_level_module.encoder.hidden_states_norms.{i}.weight")		)
						rename_keys.append((F"backbone.norm{i}.bias", F"model.pixel_level_module.encoder.hidden_states_norms.{i}.bias")		)

			# FPN
			rename_keys.append(('''sem_seg_head.layer_4.weight''', '''model.pixel_level_module.decoder.fpn.stem.0.weight''')		)
			rename_keys.append(('''sem_seg_head.layer_4.norm.weight''', '''model.pixel_level_module.decoder.fpn.stem.1.weight''')		)
			rename_keys.append(('''sem_seg_head.layer_4.norm.bias''', '''model.pixel_level_module.decoder.fpn.stem.1.bias''')		)
			for source_index, target_index in zip(range(3			,       0			,       -1		)			,       range(0			,       3		)		):
						rename_keys.append((F"sem_seg_head.adapter_{source_index}.weight", F"model.pixel_level_module.decoder.fpn.layers.{target_index}.proj.0.weight")		)
						rename_keys.append((F"sem_seg_head.adapter_{source_index}.norm.weight", F"model.pixel_level_module.decoder.fpn.layers.{target_index}.proj.1.weight")		)
						rename_keys.append((F"sem_seg_head.adapter_{source_index}.norm.bias", F"model.pixel_level_module.decoder.fpn.layers.{target_index}.proj.1.bias")		)
						rename_keys.append((F"sem_seg_head.layer_{source_index}.weight", F"model.pixel_level_module.decoder.fpn.layers.{target_index}.block.0.weight")		)
						rename_keys.append((F"sem_seg_head.layer_{source_index}.norm.weight", F"model.pixel_level_module.decoder.fpn.layers.{target_index}.block.1.weight")		)
						rename_keys.append((F"sem_seg_head.layer_{source_index}.norm.bias", F"model.pixel_level_module.decoder.fpn.layers.{target_index}.block.1.bias")		)
			rename_keys.append(('''sem_seg_head.mask_features.weight''', '''model.pixel_level_module.decoder.mask_projection.weight''')		)
			rename_keys.append(('''sem_seg_head.mask_features.bias''', '''model.pixel_level_module.decoder.mask_projection.bias''')		)

			# Transformer decoder
			for idx in range(config.decoder_config.decoder_layers		):
						# self-attention out projection
						rename_keys.append((F"sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.out_proj.weight", F"model.transformer_module.decoder.layers.{idx}.self_attn.out_proj.weight")		)
						rename_keys.append((F"sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.out_proj.bias", F"model.transformer_module.decoder.layers.{idx}.self_attn.out_proj.bias")		)
						# cross-attention out projection
						rename_keys.append((F"sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.out_proj.weight", F"model.transformer_module.decoder.layers.{idx}.encoder_attn.out_proj.weight")		)
						rename_keys.append((F"sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.out_proj.bias", F"model.transformer_module.decoder.layers.{idx}.encoder_attn.out_proj.bias")		)
						# MLP 1
						rename_keys.append((F"sem_seg_head.predictor.transformer.decoder.layers.{idx}.linear1.weight", F"model.transformer_module.decoder.layers.{idx}.fc1.weight")		)
						rename_keys.append((F"sem_seg_head.predictor.transformer.decoder.layers.{idx}.linear1.bias", F"model.transformer_module.decoder.layers.{idx}.fc1.bias")		)
						# MLP 2
						rename_keys.append((F"sem_seg_head.predictor.transformer.decoder.layers.{idx}.linear2.weight", F"model.transformer_module.decoder.layers.{idx}.fc2.weight")		)
						rename_keys.append((F"sem_seg_head.predictor.transformer.decoder.layers.{idx}.linear2.bias", F"model.transformer_module.decoder.layers.{idx}.fc2.bias")		)
						# layernorm 1 (self-attention layernorm)
						rename_keys.append((F"sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm1.weight", F"model.transformer_module.decoder.layers.{idx}.self_attn_layer_norm.weight")		)
						rename_keys.append((F"sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm1.bias", F"model.transformer_module.decoder.layers.{idx}.self_attn_layer_norm.bias")		)
						# layernorm 2 (cross-attention layernorm)
						rename_keys.append((F"sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm2.weight", F"model.transformer_module.decoder.layers.{idx}.encoder_attn_layer_norm.weight")		)
						rename_keys.append((F"sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm2.bias", F"model.transformer_module.decoder.layers.{idx}.encoder_attn_layer_norm.bias")		)
						# layernorm 3 (final layernorm)
						rename_keys.append((F"sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm3.weight", F"model.transformer_module.decoder.layers.{idx}.final_layer_norm.weight")		)
						rename_keys.append((F"sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm3.bias", F"model.transformer_module.decoder.layers.{idx}.final_layer_norm.bias")		)

			rename_keys.append(('''sem_seg_head.predictor.transformer.decoder.norm.weight''', '''model.transformer_module.decoder.layernorm.weight''')		)
			rename_keys.append(('''sem_seg_head.predictor.transformer.decoder.norm.bias''', '''model.transformer_module.decoder.layernorm.bias''')		)

			# heads on top
			rename_keys.append(('''sem_seg_head.predictor.query_embed.weight''', '''model.transformer_module.queries_embedder.weight''')		)

			rename_keys.append(('''sem_seg_head.predictor.input_proj.weight''', '''model.transformer_module.input_projection.weight''')		)
			rename_keys.append(('''sem_seg_head.predictor.input_proj.bias''', '''model.transformer_module.input_projection.bias''')		)

			rename_keys.append(('''sem_seg_head.predictor.class_embed.weight''', '''class_predictor.weight''')		)
			rename_keys.append(('''sem_seg_head.predictor.class_embed.bias''', '''class_predictor.bias''')		)

			for i in range(3		):
						rename_keys.append((F"sem_seg_head.predictor.mask_embed.layers.{i}.weight", F"mask_embedder.{i}.0.weight")		)
						rename_keys.append((F"sem_seg_head.predictor.mask_embed.layers.{i}.bias", F"mask_embedder.{i}.0.bias")		)
			# fmt: on

			return rename_keys
def __UpperCAmelCase   (					A					:			List[Any]			,       A					:			Union[str, Any]			,       A					:			Any		)    ->   int:
			UpperCAmelCase_      :       Tuple          =       dct.pop(A		)
			UpperCAmelCase_      :       Optional[Any]          =       val
def __UpperCAmelCase   (					A					:			Any			,       A					:			Optional[Any]		)    ->   Union[str, Any]:
			UpperCAmelCase_      :       Optional[int]          =       [int(backbone_config.embed_dim * 2**i		) for i in range(len(backbone_config.depths		)		)]
			for i in range(len(backbone_config.depths		)		):
						UpperCAmelCase_      :       Optional[Any]          =       num_features[i]
						for j in range(backbone_config.depths[i]		):
									# fmt: off
									# read in weights + bias of input projection layer (in original implementation, this is a single matrix + bias)
									UpperCAmelCase_      :       Optional[int]          =       state_dict.pop(F"backbone.layers.{i}.blocks.{j}.attn.qkv.weight"		)
									UpperCAmelCase_      :       List[str]          =       state_dict.pop(F"backbone.layers.{i}.blocks.{j}.attn.qkv.bias"		)
									# next, add query, keys and values (in that order) to the state dict
									UpperCAmelCase_      :       Union[str, Any]          =       in_proj_weight[:dim, :]
									UpperCAmelCase_      :       Optional[int]          =       in_proj_bias[: dim]
									UpperCAmelCase_      :       Dict          =       in_proj_weight[
									    dim : dim * 2, :
									]
									UpperCAmelCase_      :       int          =       in_proj_bias[
									    dim : dim * 2
									]
									UpperCAmelCase_      :       int          =       in_proj_weight[
									    -dim :, :
									]
									UpperCAmelCase_      :       Union[str, Any]          =       in_proj_bias[-dim :]
									# fmt: on
def __UpperCAmelCase   (					A					:			Optional[Any]			,       A					:			Optional[Any]		)    ->   List[str]:
			# fmt: off
			UpperCAmelCase_      :       str          =       config.decoder_config.hidden_size
			for idx in range(config.decoder_config.decoder_layers		):
						# read in weights + bias of self-attention input projection layer (in the original implementation, this is a single matrix + bias)
						UpperCAmelCase_      :       Any          =       state_dict.pop(F"sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.in_proj_weight"		)
						UpperCAmelCase_      :       Optional[Any]          =       state_dict.pop(F"sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.in_proj_bias"		)
						# next, add query, keys and values (in that order) to the state dict
						UpperCAmelCase_      :       List[str]          =       in_proj_weight[: hidden_size, :]
						UpperCAmelCase_      :       Union[str, Any]          =       in_proj_bias[:config.hidden_size]
						UpperCAmelCase_      :       Optional[Any]          =       in_proj_weight[hidden_size : hidden_size * 2, :]
						UpperCAmelCase_      :       int          =       in_proj_bias[hidden_size : hidden_size * 2]
						UpperCAmelCase_      :       List[str]          =       in_proj_weight[-hidden_size :, :]
						UpperCAmelCase_      :       List[str]          =       in_proj_bias[-hidden_size :]
						# read in weights + bias of cross-attention input projection layer (in the original implementation, this is a single matrix + bias)
						UpperCAmelCase_      :       Union[str, Any]          =       state_dict.pop(F"sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.in_proj_weight"		)
						UpperCAmelCase_      :       Tuple          =       state_dict.pop(F"sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.in_proj_bias"		)
						# next, add query, keys and values (in that order) to the state dict
						UpperCAmelCase_      :       Union[str, Any]          =       in_proj_weight[: hidden_size, :]
						UpperCAmelCase_      :       Union[str, Any]          =       in_proj_bias[:config.hidden_size]
						UpperCAmelCase_      :       Any          =       in_proj_weight[hidden_size : hidden_size * 2, :]
						UpperCAmelCase_      :       Optional[int]          =       in_proj_bias[hidden_size : hidden_size * 2]
						UpperCAmelCase_      :       int          =       in_proj_weight[-hidden_size :, :]
						UpperCAmelCase_      :       Optional[int]          =       in_proj_bias[-hidden_size :]
			# fmt: on
def __UpperCAmelCase   (					)    ->   torch.Tensor:
			UpperCAmelCase_      :       Optional[int]          =       '''http://images.cocodataset.org/val2017/000000039769.jpg'''
			UpperCAmelCase_      :       List[str]          =       Image.open(requests.get(A			,       stream=A		).raw		)
			return im
@torch.no_grad()
def __UpperCAmelCase   (					A					:			str			,       A					:			str			,       A					:			str			,       A					:			bool = False		)    ->   List[Any]:
			UpperCAmelCase_      :       List[str]          =       get_maskformer_config(A		)

			# load original state_dict
			with open(A			,       '''rb'''		) as f:
						UpperCAmelCase_      :       int          =       pickle.load(A		)
			UpperCAmelCase_      :       List[str]          =       data['''model''']

			# for name, param in state_dict.items():
			#     print(name, param.shape)

			# rename keys
			UpperCAmelCase_      :       Optional[int]          =       create_rename_keys(A		)
			for src, dest in rename_keys:
						rename_key(A			,       A			,       A		)
			read_in_swin_q_k_v(A			,       config.backbone_config		)
			read_in_decoder_q_k_v(A			,       A		)

			# update to torch tensors
			for key, value in state_dict.items():
						UpperCAmelCase_      :       Any          =       torch.from_numpy(A		)

			# load 🤗 model
			UpperCAmelCase_      :       str          =       MaskFormerForInstanceSegmentation(A		)
			model.eval()

			for name, param in model.named_parameters():
						print(A			,       param.shape		)

			UpperCAmelCase_    ,      UpperCAmelCase_      :       List[str]          =       model.load_state_dict(A			,       strict=A		)
			assert missing_keys == [
			    "model.pixel_level_module.encoder.model.layernorm.weight",
			    "model.pixel_level_module.encoder.model.layernorm.bias",
			]
			assert len(A		) == 0, F"Unexpected keys: {unexpected_keys}"

			# verify results
			UpperCAmelCase_      :       Optional[int]          =       prepare_img()
			if "vistas" in model_name:
						UpperCAmelCase_      :       Optional[Any]          =       6_5
			elif "cityscapes" in model_name:
						UpperCAmelCase_      :       int          =       6_5_5_3_5
			else:
						UpperCAmelCase_      :       int          =       2_5_5
			UpperCAmelCase_      :       int          =       True if '''ade''' in model_name else False
			UpperCAmelCase_      :       str          =       MaskFormerImageProcessor(ignore_index=A			,       reduce_labels=A		)

			UpperCAmelCase_      :       List[Any]          =       image_processor(A			,       return_tensors='''pt'''		)

			UpperCAmelCase_      :       List[Any]          =       model(**A		)

			print('''Logits:'''			,       outputs.class_queries_logits[0, :3, :3]		)

			if model_name == "maskformer-swin-tiny-ade":
						UpperCAmelCase_      :       str          =       torch.tensor(
						    [[3.6353, -4.4770, -2.6065], [0.5081, -4.2394, -3.5343], [2.1909, -5.0353, -1.9323]]		)
			assert torch.allclose(outputs.class_queries_logits[0, :3, :3]			,       A			,       atol=1e-4		)
			print('''Looks ok!'''		)

			if pytorch_dump_folder_path is not None:
						print(F"Saving model and image processor to {pytorch_dump_folder_path}"		)
						Path(A		).mkdir(exist_ok=A		)
						model.save_pretrained(A		)
						image_processor.save_pretrained(A		)

			if push_to_hub:
						print('''Pushing model and image processor to the hub...'''		)
						model.push_to_hub(F"nielsr/{model_name}"		)
						image_processor.push_to_hub(F"nielsr/{model_name}"		)


if __name__ == "__main__":
						_UpperCamelCase						:						str  =					argparse.ArgumentParser()
						# Required parameters
						parser.add_argument(
						    '--model_name',
						    default='maskformer-swin-tiny-ade',
						    type=str,
						    help=('Name of the MaskFormer model you\'d like to convert',),
						)
						parser.add_argument(
						    '--checkpoint_path',
						    default='/Users/nielsrogge/Documents/MaskFormer_checkpoints/MaskFormer-Swin-tiny-ADE20k/model.pkl',
						    type=str,
						    help='Path to the original state dict (.pth file).',
						)
						parser.add_argument(
						    '--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model directory.'
						)
						parser.add_argument(
						    '--push_to_hub', action='store_true', help='Whether or not to push the converted model to the 🤗 hub.'
						)

						_UpperCamelCase						:						Any  =					parser.parse_args()
						convert_maskformer_checkpoint(
						    args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
						)










'''simple docstring'''





_UpperCamelCase						:						List[str]  =					[sum(int(c, 10) ** 2 for c in i.__str__()) for i in range(100_000)]
def __UpperCAmelCase   (					A					:			int		)    ->   int:
			UpperCAmelCase_      :       str          =       0
			while number:
						# Increased Speed Slightly by checking every 5 digits together.
						sum_of_digits_squared += DIGITS_SQUARED[number % 1_0_0_0_0_0]
						number //= 1_0_0_0_0_0

			return sum_of_digits_squared


# There are 2 Chains made,
# One ends with 89 with the chain member 58 being the one which when declared first,
# there will be the least number of iterations for all the members to be checked.

# The other one ends with 1 and has only one element 1.

# So 58 and 1 are chosen to be declared at the starting.

# Changed dictionary to an array to quicken the solution
_UpperCamelCase						:						list[bool | None]  =					[None] * 10_000_000
_UpperCamelCase						:						List[str]  =					True
_UpperCamelCase						:						Tuple  =					False
def __UpperCAmelCase   (					A					:			int		)    ->   bool:

			if CHAINS[number - 1] is not None:
						return CHAINS[number - 1]  # type: ignore

			UpperCAmelCase_      :       Dict          =       chain(next_number(A		)		)
			UpperCAmelCase_      :       int          =       number_chain

			while number < 1_0_0_0_0_0_0_0:
						UpperCAmelCase_      :       Optional[Any]          =       number_chain
						number *= 1_0

			return number_chain
def __UpperCAmelCase   (					A					:			int = 1_0_0_0_0_0_0_0		)    ->   int:
			for i in range(1			,       A		):
						if CHAINS[i] is None:
									chain(i + 1		)

			return CHAINS[:number].count(A		)


if __name__ == "__main__":
						import doctest

						doctest.testmod()
						print(f'''{solution() = }''')


